Wofford College 300/Reduced... · Web viewReduced Frequency of Knowledge of Results Enhances Skill Learning in Rats as in Humans Alliston K. Reid and Paige G. Bolton Swafford Wofford

Running head: KNOWLEDGE OF RESULTS IN RATS 1

Reduced Frequency of Knowledge of Results Enhances Skill Learning in Rats as in Humans

Alliston K. Reid and Paige G. Bolton Swafford

Wofford College

Author Note

Alliston K. Reid and Paige G. Bolton Swafford, Department of Psychology, Wofford College,

Spartanburg, SC, 29303 USA.

We thank students enrolled in Learning & Adaptive Behavior at Wofford College for

helping with the subjects in this experiment. We also thank Elizabeth Kyonka and James Mazur

for fruitful discussions about this research. We are grateful to Wofford College for funding this

research. Preliminary versions of this work were presented at the annual meeting of the XXX

International Conference of the Spanish Society for Comparative Psychology, Ávila, Spain in

2018 and at the annual meeting of the Association for Behavior Analysis International in 2019.

Address correspondence to: Alliston Reid, Department of Psychology, Wofford College,

429 N. Church St., Spartanburg, SC 29303, USA. E-mail: [email protected]

KNOWLEDGE OF RESULTS IN RATS 2

Abstract

Macphail’s (1985) null hypothesis challenged researchers to demonstrate any differences in

intelligence between vertebrate species. Rather than focus on differences, we asked whether rats

would show the same unexpected, counterintuitive features of skill learning observed in humans:

Factors that degrade performance during acquisition often enhance performance in a subsequent

retention/autonomy phase. Providing post-trial “knowledge of results” (KR) on 30%-67% of

trials instead of 100% degrades accuracy, yet increases retention in a subsequent phase without

KR. We tested this feature by providing three groups of rats with KR on every trial (100% KR),

67% KR, or 0% KR. We also provided operant feedback in every trial for completing the left-

right lever-press skill (food for correct sequences, timeout for all others). In the autonomy phase,

we assessed their ability to complete the skill independently—in the absence of differential cues

and KR feedback. In agreement with human performance in the autonomy phase, 67% KR

yielded higher skill accuracy than providing 100% KR. Also, providing 67% KR improved skill

accuracy above that observed with operant feedback alone (0% KR). Rather than degrading

performance during acquisition, the 67% KR condition yielded unexpected higher accuracy than

the other conditions. Accuracy increased systematically across our extended acquisition phase,

which provided each rat with over 3600 trials compared to 20-30 trials for human studies.

Providing limited KR promoted skill learning in rats as it does in humans, consistent with the

conjecture that both species share common learning processes. Introducing difficulties to rats

during training improved their autonomy.


Reduced Frequency of Knowledge of Results Enhances Skill Learning in Rats as in Humans

Skill learning has been widely studied in humans, but far less in rats. In the last few

decades, cognitive psychologists have often focused on a counterintuitive feature of skill

learning in humans that is related to the historical distinction between learning and performance

dating back to Thorndike (1927). A highly cited empirical review by Schmidt and Bjork (1992)

described this common feature across several motor- and verbal-learning paradigms: Factors that

degrade performance during acquisition often enhance performance in a subsequent retention

condition (e.g., Bjork & Bjork, 2014; Kantak & Winstein, 2012; Winstein & Schmidt, 1990;

Wulf & Schmidt, 1989). They argued that introducing difficulties for the learner during

acquisition can enhance later retention because they trigger encoding and retrieval processes that

support learning, comprehension, and memory.

As implied above, skill-learning studies usually consist of two phases: an acquisition

phase in which the skill is acquired in the presence of useful stimuli or feedback from

performance, and a retention phase in which retention of the skill is measured when those cues

and feedback are no longer provided. In studies with nonhumans such as rats or pigeons, we

usually call this retention phase an “autonomy” phase because we are interested in the degree to

which the animal can complete the skill independently—in the absence of earlier guiding cues or

informative feedback. Humans typically seek autonomy as we acquire behavioral skills related to

sports, driving, military training, dance, our jobs, and most games of skill. Applied behavior

analysis often promotes skill learning and autonomy in children with developmental disabilities.

Extensive practice of behavioral skills usually leads to autonomy in humans and trained animals,

even rats and pigeons (e.g., Helton, 2007a, b; Reid, DeMarco, Smith, Fort, & Cousins, 2013;

Reid, Nill, & Getz, 2010).


Behavioral skill learning (as distinguished from cognitive skills such as playing chess)

could focus on the roles of (a) antecedent stimuli, or on (b) feedback from performance. Rat

studies have focused on anticipatory cues, such as transfer of stimulus control from

discriminative stimuli to new exteroceptive or endogenous cues that develop during practice. For

example, Mackintosh (1965, 1974) described examples of transfer to proprioceptive control as

rats run through mazes and suggested that “if sufficient training were given on a maze problem,

control was gradually transferred from exteroceptive to proprioceptive stimuli” (1974, p. 554).

Several rat and pigeon studies have observed the same counterintuitive feature of human skill

learning described above: Less effective cues and more difficult behavioral skills degrade

accuracy during acquisition phases, yet enhance accuracy in a subsequent autonomy phase (e.g.,

Rats: Reid et al., 2010, 2013; Reid, Rapport, & Le, 2013; Reid, Futch, Ball, Knight, & Tucker,

2017; Pigeons: Fox, Reid, & Kyonka, 2014; Reid, Folks, & Hardy, 2014). We often described

these observations with the metaphor: “Holding your child’s hand too much may delay or

prevent autonomy.”

In contrast, skill-learning studies with humans have focused mostly on feedback from

performance. While many types of feedback may be provided, the most influential type has been

“knowledge of results” (KR): post-trial feedback about the success of a behavior during skill

acquisition (Adams, 1987; Newell, 1991; Wolpert, Diedrichsen, & Flanagan, 2011; Wulf &

Schmidt, 1989; Wulf & Shea, 2002). One might predict that providing KR on every trial (100%

KR) would lead to faster acquisition and stronger retention. However, Winstein and Schmidt

(1990) demonstrated a counterintuitive feature of providing different relative frequencies of KR

as feedback to human participants: Practice with reduced 50% KR improved skill retention over

100% KR, even though performance during acquisition suffered. This observation has been


widely (although not universally) replicated with different types of skill learning in humans.

Similarly, Wulf and Schmidt (1989) evaluated the effect of KR on a more complex behavioral

skill (generalized motor programs) by comparing 67% KR versus 100% KR. Practice with

reduced 67% KR was more beneficial to the transfer of skill learning even with this more

complex behavioral skill. These studies were consistent with Schmidt and Bjork’s (1992) claim

that introducing difficulties for the learner during acquisition can enhance later retention.

Unfortunately, most human studies of skill learning ignore the other type of feedback: the

operant consequences of responding such as reinforcement and punishment effects. Studies with

rats or pigeons consistently specify these operant consequences. To our knowledge, no published

rat or pigeon studies have included KR feedback along with operant feedback. Therefore, it is

not known whether rats and humans would respond similarly to acquisition procedures that

provide KR feedback. If rats and humans show not only the same basic features of skill learning,

but they also show the same counterintuitive features, this would seem like much stronger

evidence that skill learning in rats and humans may involve similar processes. Therefore, the

purpose of this study was to begin to answer that very question.

This experiment combined operant feedback with KR feedback, so it is helpful to clarify

the difference between the procedures, as well as the ways in which each affects skill acquisition

and retention. However, our understanding of both terms and their mechanisms of action have

changed substantially over the last century. Thorndike (1927) considered both types of post-trial

feedback to be consistent with his Law of Effect: food delivery and KR indicating performance

successes would both strengthen the stimulus-response connection, whereas extinction and KR

indicating performance errors would both weaken the connection. This view changed

substantially after Salmoni, Schmidt, and Walter (1984) reviewed many studies of skill learning


that provided KR feedback to human participants and asked: How does KR work? They

especially emphasized the surprising observation that reduced frequencies of KR typically led to

greater retention of motor skills than when KR was presented on every trial. Their “guidance

hypothesis” emphasized the informative, goal-directed properties of KR feedback, which was

more compatible with information-processing views of human cognition than the reinforcing

properties of feedback. Today, KR in human studies is generally believed to provide

opportunities to allocate cognitive resources such as attention and encoding processes. Several

recent articles have expressed doubts as to whether laboratory animals would have the

information-processing capacities to benefit from KR feedback (e.g., Adams, 1987; Wulf &

Shea, 2002; Wulf & Schmidt, 1989). This interesting claim assumes that Macphail’s (1985) null

hypothesis is wrong, but (until now) no studies with laboratory animals have tested the

assumption that laboratory animals and humans would react differently to skill learning

procedures that provide KR.

Our behavioral skill was a simple left-right (L−R) lever-press sequence guided by blue

and green panel lights above the two levers. All other sequences were errors. The second press

terminated the trial and provided both operant and KR feedback. Operant feedback was always

immediate and preceded KR feedback. Following the KR procedures of Wulf and Schmidt

(1989) in a between-groups design, we compared the effects of 67% KR with that of 100% KR.

As a control condition, we also included a 0% KR group that received operant feedback every

trial, but never any KR feedback, in both acquisition and autonomy phases. Normally, pellet

delivery and KR feedback would be perfectly correlated for the 100% KR group. To distinguish

between these two factors, we degraded this correlation by providing reinforcement for correct

trials on a random ratio 2 (RR-2) schedule of reinforcement. If KR affects rats in the same way


as in humans, then sequence accuracy during the autonomy phase should be higher for the 67%

KR group than the 100% KR group, but accuracy for the 67% KR group during the acquisition

phase should be lower than the 100% KR group. If providing KR promotes skill learning beyond

the effects of operant feedback alone, then accuracy for the 67% KR group should exceed that of

the 0% KR group.

Method

Subjects

Twenty-six naïve 4-month-old female Long Evans rats (Rattus norvegicus) were housed

individually in a facility that maintained constant temperature and humidity on a 12:12-h

light:dark cycle. Body weight was maintained at 80-85% of free-feeding weight by providing

supplemental food (Tekland Rodent Diet) after daily sessions in home cages, where water was

freely available.

Apparatus

We utilized four standard Med Associates operant chambers for rats, measuring 30 × 24

× 22 cm. Each chamber was located inside an isolation chamber containing a ventilation fan and

a 7-W nightlight. A sound generator produced constant 65-db white noise in each chamber. Each

operant chamber contained two retractable levers on the front wall and two nonretractable levers

on the rear wall. Each pair of levers was separated by 16.5 cm, center to center, and located 6 cm

above the floor. The 5 × 5 cm magazine hopper was centered between the two response levers on

the front wall, 3 cm above the floor. A 2.5-cm 28-V white panel lamp wase located 2.5 cm above

the two front levers, and a 28-V houselight was located at the center top of the rear wall. In some

conditions, 28-V blue or green LEDs replaced the two white panel lamps on the rear wall. The

wavelengths of these LEDs (blue: 465 nm, green: 515 nm) were selected to approximately match


the peak photopigment sensitivity of the UV and M cones (358 nm and 510 nm, respectively) in

Long-Evans rats (Jacobs, Fenwick, & Williams, 2001). A Sonalert was available to produce

1000-Hz tones. The pellet dispenser provided 45-mg Research Diet pellets. All four chambers

were controlled by a single Lenovo personal computer located in an adjacent room and

programmed in MED-PC IV, which implemented all experimental conditions and recorded every

event and its time of occurrence with 10-ms resolution.

Procedure

We randomly assigned the 26 naïve rats to three experimental groups of 8-9 rats each.

The groups differed in the frequency of qualitative KR feedback about the accuracy of

responding, which was provided by the two white front panel lamps and the Sonalert tone for

responding on the two rear levers in a discrete-trials procedure. The 0% KR group received no

KR on any trial; the 100% KR group received KR on every trial; and the 67% KR group received

KR on 67% of the trials.

We exposed all three groups to a sequence of training procedures that reinforced lever

pressing, first on the front wall and subsequently on the rear wall. Once lever-press training was

completed, the experiment consisted of two experimental phases on the rear wall. The purpose of

the 30-session Acquisition Phase was to allow each group of rats to learn the left-right (L–R)

lever-press sequence (the skill) on the rear wall, guided by blue (left) and green (right) LED

panel lights. In the 10-session Autonomy Phase, the reinforcement contingencies were

unchanged, but all KR was eliminated, and the rear blue and green LED panel lights were

replaced with white LEDs. Our primary measure was percentage L–R accuracy, which allowed

us to assess (a) the speed in which the three groups learned the L–R sequence during the


Acquisition Phase, and (b) the degree of L–R autonomy once we eliminated the differential

stimuli and KR in the Autonomy Phase.

Training.

Lever-press training. We exposed all rats to an autoshaping procedure for three sessions.

The procedure inserted the right front retractable lever (adjacent to the food hopper) into the

chamber 8 s before delivering a pellet, followed by a 52-s intertrial interval (ITI). Each press on

that lever or on any of the three other levers was reinforced, independent of the 8-s lever

insertion. A 100-ms tone “beep” accompanied all lever presses that produced pellet delivery in

each training condition. Sessions ended with the earlier of 45 min or 80 reinforcers.

We next exposed each rat to a shaping procedure in which presses to the front right

retractable lever or either rear lever produced pellet delivery. The white panel lamp over the front

right lever was illuminated, but the rear panel lamps remained off. Pellet deliveries were

followed by 3-s ITI, signaled by extinguishing that panel light and the houselight. Sessions ended

with the earlier of 30 min or 45 pellets. This training procedure terminated when the rat earned

45 pellets/session for three sessions. All subsequent conditions required subjects to press levers

on the rear wall, so the levers on the front wall were retracted for the rest of the experiment.

Rear Wall. We exposed each subject to training sessions of fixed ratio-1 (FR-1) for

pressing the right lever on the rear wall. The green panel light above that lever was illuminated,

while the other panel lamps remained extinguished. Food delivery was followed by a 3-s ITI,

signaled by extinguishing the panel light and the houselight. Sessions ended with the earlier of

30 min or 45 reinforcers. This training procedure terminated when the rat earned 45

pellets/session for three sessions.


Following this training, we exposed each subject to training sessions for pressing the left

lever on the rear wall on FR-1. The blue panel light above that lever was illuminated, while the

other panel lamps remained extinguished. Otherwise, conditions were the same as training on the

right lever. The purpose of these rear-wall training sessions was to ensure that all subjects

received approximately equal exposure to the reinforcement conditions on the rear wall before

the experiment proper began, given that subjects required differing amounts of lever-press

training on the front wall.

Experiment: L–R Acquisition Phase.

The Acquisition Phase lasted 30 sessions for all subjects. It required subjects to complete

a L–R lever-press sequence guided by blue (left) and green (right) panel lights in a discrete-trials

procedure. No feedback about response accuracy was provided until two lever presses occurred.

At the beginning of each trial, the houselight and the rear blue (left) and green (right) panel lights

were illuminated. Pressing the left lever turned off the left panel light, leaving only the right

(green) panel light on. Similarly, pressing the right lever first would turn off the right panel light,

leaving only the left (blue) panel light on. This was intended to reduce perseveration on either

lever. Completion of the correct L–R response sequence turned off the houselight and any panel

lights and produced the 100-ms tone “beep”. In addition, a food pellet was delivered with

probability 0.5, producing a random ratio 2 (RR-2) reinforcement schedule. The RR-2 schedule

was intended to degrade the normal correlation in discrete-trials procedures containing both food

delivery and KR feedback. Incorrect trials contained any other 2-response sequence (L–L, R–L,

R–R) and resulted in a 3-s timeout (TO) in which the houselight and panel lights were

extinguished. Responding during TO had no programmed consequences. The next trial then

began with the illumination of the houselight and the blue and green panel lights. Sessions lasted


for the earlier of 45 min or until 120 correct L–R trials occurred (producing approximately 60

pellets on the RR-2 schedule).

Following the operant consequences described above, the three experimental groups

received slightly delayed (200-ms) KR feedback with probabilities identified by the name of

each group: 0% KR, 67% KR, or 100% KR. As Figure 1 illustrates, the qualitative probabilistic

KR feedback was different for correct trials and incorrect trials. Correct sequences produced KR

in which both front white panel lights turned on for 2 s, while the Sonalert beeped four times in

that 2-s interval (each 0.25-s on, 0.25-s off). Following incorrect sequences, the Sonalert sounded

for a full second, and the front panel lights remained off.

Experiment: Autonomy Phase.

The 10-session Autonomy Phase was identical to the Acquisition Phase except: (a) The

blue and green LED panel lights were replaced by identical white LED lights, and (b) No KR

feedback was provided in any trial to any group. The lamps functioned the same way within

trials as before (e.g., during ITI and TO), but the white lights above both levers provided no

discriminative cues to influence lever selection. The autonomy condition assessed how well each

rat could complete the L–R sequence independently, without the differential cues provided by

the panel lights or the potential influence of KR feedback.

Results

Figure 2 depicts L−R sequence accuracy for the three KR groups across the acquisition

and autonomy phases, separated by the vertical dotted line. We adopted an alpha level of .05 for

all statistical tests.

Acquisition Phase. We used a mixed-effects factorial ANOVA across the 30 sessions

of the Acquisition Phase to assess two potential main effects (group, sessions) and a potential


group × sessions interaction. We observed a significant main repeated-measures effect of

sessions, F(29, 667) = 20.105, p < .001, ηp2 = .446, confirming that accuracy generally increased

across acquisition sessions. We also observed a statistically significant group × sessions

interaction, indicating that some groups learned faster or slower than average, F(58, 667) =

1.531, p = .008, ηp2 = .118. This significant interaction should be expected to qualify any main

effect of group. Even so, the mixed-effects ANOVA showed a nearly significant main effect of

group, F(2, 23) = 3.369, p = .052, ηp2 = .227. Figure 3 depicts the relevant pairwise comparisons

between groups. An LSD post hoc test demonstrated that accuracy for the 67% KR group

increased faster than for the 100% KR group, p = .016, easily visible in the top panel of Figure 3.

There were no significant between-group differences during the Acquisition Phase between the

0% KR group and the 67% KR group (middle panel), nor between the 0% KR group and the

100% KR group.

Autonomy Phase. Figure 2 illustrates continued increasing accuracy through

autonomy sessions. A mixed-effects factorial ANOVA demonstrated a main effect of sessions in

the Autonomy Phase, Wilks Lambda = .262, F(9, 15) = 4.698, p = .004, ηp2 = .738, observed

power = .964. The sessions × group interaction was not statistically significant, indicating that

the groups learned similarly during the Autonomy Phase, Wilks Lambda = .442, F(18, 30)

= .839, p = .646. The main effect of group was statistically significant, F(2, 23) = 7.112, p

= .004, ηp2 = .382, observed power = .895. The Autonomy Phase in the top panel of Figure 3

clearly shows that L−R accuracy for the 67% KR group was significantly greater than the 100%

KR group, as confirmed with the LSD post hoc test, p = .001. Similarly, the LSD post hoc test

indicated that the 67% KR group achieved significantly higher accuracy during the Autonomy

Phase than the 0% KR group (middle panel), p = .033. The 0% KR group in the bottom panel


appears slightly higher than the 100% KR group, but the difference was not statistically

significant, p = .19.

Discussion

The purpose of this study was to begin to answer the question: Does KR affect rats the

same way as with humans? Many studies have demonstrated the counterintuitive feature of

human skill learning that practice with reduced relative frequency of KR improves skill retention

over 100% KR, even though performance during acquisition often suffers (e.g., Bjork & Bjork,

2014; Kantak & Winstein, 2012; Schmidt & Bjork, 1992; Winstein & Schmidt, 1990; Wulf &

Schmidt, 1989). Our rats demonstrated the same clear effect. Accuracy in the Autonomy Phase

for the 67% KR group was greater than in the 100% KR group (Fig. 3, top panel): significantly

greater accuracy with reduced KR feedback. The rats replicated the counterintuitive feature of

skill learning that has come to define how motor skill learning occurs in humans. Yet

performance during our Acquisition Phase did not suffer: Our 67% KR group yielded higher

accuracy than the 100% KR group throughout both phases. This difference between rats and

humans is surely due to the procedural differences in the number of trials experienced during

acquisition. Human studies typically provide only about 20-30 acquisition trials (total), whereas

our rats received an average of 3615 trials—more than 100 times as many (about 120 trials per

session for 30 sessions). Performance in human KR studies improves for every group during

acquisition phases (not only in the brief retention phase), consistent with the power law of

practice. Therefore, one should expect improvement to continue when the duration of the

Acquisition Phase is greatly extended, as in this experiment with rats.

To our knowledge, this is the first experiment with rats that explicitly combined operant

and KR feedback. By including a 0% KR group that never received any KR feedback, our


procedure allowed us to measure whether the addition of KR feedback influences accuracy

beyond that of operant feedback alone. Figure 3 shows that the 67% KR group produced

significantly higher accuracy than did the 0% KR group during the Autonomy Phase. Thus,

providing 67% KR feedback did improve autonomy above that of operant feedback alone.

KR experiments with humans have repeatedly demonstrated that acquisition is improved

by providing some (“but not too much”) KR feedback. However, they do not normally provide a

0% KR baseline condition for comparison. We just observed that providing 67% KR was not

“too much” compared to 0% KR because 67% KR was still beneficial. The bottom panel

assesses whether 100% KR was “too much” feedback, but our differences were not statistically

significant. Our 0% KR group visually appeared to have greater accuracy than did the 100% KR

group, implying that 100% KR may be “too much” to be beneficial for rats. An understanding of

what entails “too much” feedback will require additional research. Nevertheless, our tentative

observation is compatible with several hypotheses designed to explain how KR on every trial is

less effective than less frequent KR, such as the guidance hypothesis (Salmoni et al., 1984) and

explanations based on information processing. Referring to human studies, Schmidt and Bjork

(1992) suggested that frequent feedback could block important information processing activities

from occurring during the acquisition phase that are required for learning and producing

effective performance at retention. In contrast, when KR is given intermittently during the

acquisition phase, human learners may be more able to evaluate performance in the absence of

KR, and thus perform better in the retention phase (Winstein & Schmidt, 1990).

Schmidt and Bjork (1992) summarized procedures that share a common principle for

learning of motor and verbal skills in humans: “Introducing difficulties for the learner can

enhance training” (p. 209). Providing reduced KR was one of these procedures (as we have


described), but others did not include KR at all. Bjork and Bjork (2014) recently described this

principle as “Creating desirable difficulties to enhance learning” (p. 56). This principle also

applies to acquisition of behavioral skills in rats and pigeons. As mentioned earlier, several rat

and pigeon studies have observed the same counterintuitive feature of human skill learning: Less

effective cues and more difficult behavioral skills degrade accuracy during acquisition phases, yet

enhance accuracy in the subsequent autonomy phase (e.g., Rats: Reid et al., 2010, 2013, 2017;

Reid, Rapport, & Le, 2013; Pigeons: Fox et al., 2014; Reid et al., 2014).

It is becoming increasingly apparent that skill learning in rats and humans share certain

consistent features—even counterintuitive features. Some researchers may assume that rats

would not have the cognitive abilities to benefit from KR feedback (e.g., Wulf & Schmidt, 1989;

Wulf & Shea, 2002) or that the mechanisms for learning in humans may not be the same as those

for animal conditioning with reinforcement (e.g., Winstein & Schmidt, 1990). Nevertheless, our

rats did replicate the major findings of KR procedures widely documented in human skill

learning. Identifying whether these similarities are due to common general principles of skill

learning will require much more research, including explorations of the various forms of KR

known to affect skill learning in humans (Mazur, 2006; Winstein, 1991). We believe that this

research will be worth the effort. We propose that experimental designs with animals (e.g., rats,

pigeons, dogs) with clearly specified combinations of operant and KR feedback (i.e., Fig. 1) have

the potential to more clearly define the relationship between (and the importance of) the two

classes of feedback that greatly influence acquisition in animals and humans. This discovery

would be a substantial improvement in our understanding of skill acquisition, even across

species.


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Figure 1. Timing diagram for operant feedback and KR feedback, depicted in different fonts. Operant feedback is depicted in black text with white background, whereas KR feedback is depicted in white text with black background. See procedure for other details.

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Figure 2. Percentage accuracy of the L−R behavioral skill for Groups 0% KR, 67% KR, and 100% KR across the 30 sessions of the Acquisition Phase and 10 sessions of the Autonomy Phase. Error bars depict standard error of the mean.

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Figure 3. Pairwise comparisons of the 0% KR, 67% KR, and 100% KR groups. Each panel depicts the percentage accuracy of the L−R behavioral skill across sessions of the acquisition and autonomy phases. Error bars depict standard error of the mean.

Documents

Wofford College 300/Reduced... · Web viewReduced Frequency of Knowledge of Results Enhances Skill Learning in Rats as in Humans Alliston K. Reid and Paige G. Bolton Swafford Wofford